Reply to Comment on "TVOR: Finding Discrete Total Variation Outliers among Histograms"

by   Nikola Banić, et al.

In this paper, we respond to a critique of one of our papers previously published in this journal, entitled "TVOR: Finding Discrete Total Variation Outliers among Histograms". Our paper proposes a method for smoothness outliers detection among histograms by using the relation between their discrete total variations (DTV) and their respective sample sizes. In this response, we demonstrate point by point that, contrary to its claims, the critique has not found any mistakes or problems in our paper, either in the used datasets, methodology, or in the obtained top outlier candidates. On the contrary, the critique's claims can easily be shown to be mathematically unfounded, to directly contradict the common statistical theorems, and to go against well established demographic terms. Exactly this is done in the reply here by providing both theoretical and experimental evidence. Additionally, due to critique's complaint, a more extensive research on top outlier candidate, i.e. the Jasenovac list is conducted and in order to clear any of the critique's doubts, new evidence of its problematic nature unseen in other lists are presented. This reply is accompanied by additional theoretical explanations, simulations, and experimental results that not only confirm the earlier findings, but also present new data. The source code is at


page 7

page 11


TVOR: Finding Discrete Total Variation Outliers among Histograms

Pearson's chi-squared test can detect outliers in the data distribution ...

Inapplicability of the TVOR method to USHMM Data Outlier Identification

Recent paper "TVOR: Finding Discrete Total Variation Outliers Among Hist...

Minimax rates in outlier-robust estimation of discrete models

We consider the problem of estimating the probability distribution of a ...

Contextual Outlier Interpretation

Outlier detection plays an essential role in many data-driven applicatio...

Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection

A standard method for measuring the impacts of AI on marginalized commun...

A Soft Method for Outliers Detection at the Edge of the Network

The combination of the Internet of Things and the Edge Computing gives m...

On the impact of outliers in loss reserving

The sensitivity of loss reserving techniques to outliers in the data or ...

Please sign up or login with your details

Forgot password? Click here to reset